Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Paul Irofti"'
Autor:
Florin Stoican, Paul Irofti
Publikováno v:
Algorithms, Vol 12, Iss 7, p 131 (2019)
The ℓ 1 relaxations of the sparse and cosparse representation problems which appear in the dictionary learning procedure are usually solved repeatedly (varying only the parameter vector), thus making them well-suited to a multi-parametric interpret
Externí odkaz:
https://doaj.org/article/8f64c89f8b474c5eaa398d6b1997624a
Autor:
Andrei A. Patrascu, Paul Irofti
Publikováno v:
Optimization Letters. 15:2255-2273
Supported by the recent contributions in multiple domains, the first-order splitting became algorithms of choice for structured nonsmooth optimization. The large-scale noisy contexts make available stochastic information on the objective function and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d01064d2ada09a81bb0c239d5b398cad
https://doi.org/10.1007/s11590-021-01702-7
https://doi.org/10.1007/s11590-021-01702-7
Publikováno v:
IFAC-PapersOnLine. 53:3551-3558
Anomaly detection in networked signals often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. We investigate the problem of learning graph structure representations using adaptations of dictionary lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d1dc91f3fd7d4c41050b1a484976f3b
https://doi.org/10.1016/j.ifacol.2020.12.1731
https://doi.org/10.1016/j.ifacol.2020.12.1731
Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive adversarial attacks.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::944349fb99b7b2bccff3b5c893e3b026
Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that generates at the same time the sparsest representations of data and the smal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a14e4855935935cb123627c4d8651ea
Autor:
Cristian Rusu, Paul Irofti
Publikováno v:
2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS).
Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ba6f2628995f73d473a00c846fb7b59
https://doi.org/10.1109/icpads53394.2021.00053
https://doi.org/10.1109/icpads53394.2021.00053
Autor:
Paul Irofti, Andrei Patrascu
Publikováno v:
Applied Mathematics Letters. 134:108348
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e0a5df7e145b14d9fcb4ce1b8157fe1
https://doi.org/10.1016/j.aml.2022.108348
https://doi.org/10.1016/j.aml.2022.108348
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d015d05afee2edb3c30a5f52d9c079c
Publikováno v:
Enabling AI Applications in Data Science ISBN: 9783030520663
Stochastic optimization lies at the core of most statistical learning models. The recent great development of stochastic algorithmic tools focused significantly onto proximal gradient iterations, in order to find an efficient approach for nonsmooth (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f07f713d0fb4c5d62e68ffd15717712a
https://doi.org/10.1007/978-3-030-52067-0_1
https://doi.org/10.1007/978-3-030-52067-0_1
Publikováno v:
Enabling AI Applications in Data Science ISBN: 9783030520663
Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and tho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6000c7a3364fcd64b14e4053318e4abf
https://doi.org/10.1007/978-3-030-52067-0_23
https://doi.org/10.1007/978-3-030-52067-0_23